A novel impact identification algorithm based on a linear approximation with maximum entropy
Smart Materials and Structures
Volume 25, Issue 9, 24 August 2016, Pages 095050
This article presents a novel impact identification algorithm that uses a linear approximation handled by a statistical inference model based on the maximum-entropy principle, termed linear approximation with maximum entropy (LME). Unlike other regression algorithms as Artificial Neural Networks (ANN) and Support Vector Machines (SVM), the proposed algorithm requires only one parameter to be selected and the impact is identified after solving a convex optimization problem that has a unique solution. In addition, with LME data is processed in a period of time that is comparable to the one of other algorithms. The performance of the proposed methodology is validated by considering an experimental aluminum plate. Time varying strain data is measured using four piezoceramic sensors bonded to the plate. To demonstrate the potential of the proposed approach over existing ones, results obtained via LME are compared with those of ANN and Least Square Support Vector Machines (LSSVM). The results demonstrate that with a low number of sensors it is possible to accurately locate and quantify impacts on a structure and that LME outperforms other impact identification algorithms.
Keywords: Impact identification, structural health monitoring, damage assessment, maximum entropy, linear approximation.
Original Journal Article: http://dx.doi.org/10.1088/0964-1726/25/9/095050
NOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.